The present invention relates to methods and apparatus to determine chemical, chemical-based and physical properties of hydrocarbon fuels, which are derived or synthesized from petroleum or biomass (e.g. gasoline and biodiesel, respectively).
The applications of the various classes of hydrocarbon fuels are based on their chemical, chemical-based and physical properties. In the case of gasoline, aromatic content, a chemical property, and octane number, a chemical-based property, are among the properties that are important to engine performance; for diesel fuels, cetane index is an example of chemical-based property important to performance; while for jet fuels, freeze point, flash point and viscosity are important physical properties. All properties are ultimately due to the chemical composition of each fuel, which is largely determined by the temperature range at which the fuels are collected during distillation of crude oil. The composition of the crude oil, the refinery and distillation system used, also influence the fuel composition. The properties of these fuels are typically measured using numerous chemical and physical property analyzers to classify the fuel and certify that property specifications are met for commercial use. These certified measurements are known as American Society for Testing and Materials (ASTM) methods. For example, aromatic content is determined by gas chromatography according to ASTM D1319, while flash point, the temperature at which a sample ignites, is determined by a closed-cup tester according to ASTM D93. These methods generally require 30 minutes or more to perform, are labor intensive, require controlled laboratory conditions, and are subject to human error. For example, in determining flash point according to ASTM D93, the operator must apply uniform heating and mixing to the sample, controt the heating rate, create a spark above the liquid, and read the temperature when the fuel ignites from a thermometer or a thermocouple digital display (that may have different ° F. or ° C. gradiations). According to the ASTM Subcommittee (ASTM D93 “Standard Test Methods for Flash Point by Pensky-Martens Closed Cup Tester”, D02.08.0B, 2006), flash point measurements are reproducible to a standard deviation of 5.8° C. for diesel and 4.3° C. for jet fuel samples (approximately 8.5% of the value in either case).
However, fuel specifications vary internationally, and may not meet the requirements for a particular vehicle in foreign service, such as the use of military vehicles overseas, and consequently the fuel needs to be qualified prior to use to ensure proper vehicle performance. This qualification typically includes classifying the fuel as gasoline, diesel, or jet, and determining the chemical, chemical-based, and physical properties as required for each class. Biodiesel has its own specifications, and due to the fact that there is considerable difference in the starting materials (animal fat, corn oil, fish oil, spent vegetable oil, etc.), fuel quality is even a greater issue.
Furthermore, the method and apparatus used to characterize the fuel should take into account the ambient and fuel temperature to obtain the most meaningful results, and should be sufficiently rugged to perform measurements on-site, such as at a distribution center or even a refinery. It is also preferable that the analysis be performed quickly and without sample pretreatment. These latter suggested capabilities, along with the ability to integrate into processing equipment, would also allow monitoring the separation of fuels in refining and distillation apparatus, the synthesis of biofuels in reactors, or blending of fuels in mixers. Furthermore, the ability to monitor properties in the process would allow controlling the process conditions that influence those properties, such as the temperature of a distillation column.
The apparati most capable of fulfilling these requirements are spectrometers, such as ultraviolet, visible, fluorescence, near infrared, infrared, and Raman spectrometers. This patent application describes the use of a portable Raman analyzer, at locations not restricted to chemical laboratories, to determine fuel class, grade and properties. The analyzer measures the Raman spectrum of the fuel and employs a spectral database to correlate the spectrum to the fuel class, grade and properties.
The primary use of Raman spectroscopy in industry is to quantify each chemical component in a chemical mixture. In the case of fuels, the shear number of components making up a typical petroleum fuel, on the order of several hundred (see for example Uhler et al., Molecular fingerprinting of gasoline by a modified EPA 8260 gas chromatography/mass spectrometry method, Int. J. Environ. Anal. Chem. 83, 1-20, 2003), makes the identification and quantitation of each chemical component impossible. However, several research groups have shown that qualitative chemical analysis, such as the general composition of fuel, can be determined from Raman spectra.
Kalasinsky et al. teach that Raman spectroscopy can be used to determine general hydrocarbon composition of kerosenes, specifically that the aromatic, alkane and alkene hydrocarbons are strongly associated with Raman peaks at 990-1010 cm−1, ˜1050 cm−1, and 1550-1700 cm−1, respectively (Quantitative Analysis of Kerosenes by Raman Spectroscopy,” Energy & Fuels, 3, 304-307, 1989). In a similar fashion, Chung et al. showed that Raman spectroscopy could be used to determine relative aromatic content in jet fuels by comparing the phenyl and biphenyl ring stretching modes at ˜1007 and 1386 cm−1 to the alkane CH2 wagging mode at 1450 to 1475 cm−1 (Analysis of Aviation Turbine Fuel Composition by Laser Raman Spectroscopy,” Appl. Spec., 45, 1527-1532, 1991).
In U.S. Pat. No. 5,139,334 Clark et al. (Hydrocarbon analysis based on low resolution Raman spectral analysis, 1992) teach the use of ratios of aromatic and alkane Raman peaks to predict pump octane number (PON), which, just as motor octane number and research octane number (MON and RON), is related to these chemical components. In one embodiment they use the ratio of the aromatic peak at ˜1006 cm−1 to the alkane peak at ˜1450 cm−1 to calculate PON. They show that the PON increases as the ratio of the aromatic peak intensity divided by the alkane peak intensity increases. This is easily understood by examining the octane rating for the primary chemicals of gasoline. It is composed of alkanes (paraffins), alkenes (olefins), cycloalkanes (naphthenes), and aromatics (primarily benzene, ethylbenzene, toluene and xylenes, known collectively as BTEX). The Raman peak at 1450 cm−1 is mostly due to the CH2 wagging mode of straight chain alkanes, which have very low octane ratings. In fact, n-octane has a zero octane value and is used to define the low end of the octane scale. Gasoline can contain as much as 80% alkanes. The Raman peak at 1006 cm−1 is primarily due to toluene, but benzene, ethylbenzene and meta-xylene also produce intense peaks close to this wavenumber. These single ring aromatics have octane ratings well over 100 (see for example Modern Petroleum Technology; 5th Edition Part II; Edited by G. D Hobson, Wiley 1984, page 786), and are often added, up to the regulated limit of 35%, to increase the octane rating of gasoline as part of the blending process (for example see Muller, New method produces accurate octane blending values, Oil & Gas Journal, 23, 80-90, 1992). Consequently one would expect that a gasoline with a PON of 94 will have a higher 1006 cm−1 to 1450 cm−1 ratio than a gasoline with a PON of 86. It is not surprising that a ratio of the integrated area under the 200 to 2000 cm−1 spectral region to the 2000 to 3500 cm−1 spectral region produces a similar trend, since the lower region is dominated by the aromatic contributions, while the higher region is dominated by alkane contributions (CH2 and CH3 stretching modes).
The vapor pressure of a gasoline indicates the ease at which it can be combusted. It is typically measured at 100° F. and reported as the Reid vapor pressure (RVP). The RVP of gasoline is dominated by the lower molecular weight fractions, butanes and pentanes, and is regulated at near 9 psi. It is often adjusted by the addition of n-butane, which has an RVP of 51.6 psi. Although the RVP could be viewed as a physical property of gasoline, it in fact largely depends on the amount of n-butane. And just like determining octane numbers by measuring aromatic content, RVP can be determined by measuring n-butane content based on the intensity of its unique Raman C-C stretching mode at ˜830 cm−1.
Improvements in calculating both the octane (MON, RON and PON) and Reid vapor pressure values can be obtained by including more Raman spectral features and weighting their contributions to the property of interest in the form of a linear or non-linear combination of features. This mathematical approach coupled with statistical treatment of the chemical data (spectra and property values) is generally referred to as chemometrics. The use of chemometrics to improve correlations between Raman spectra and fuel properties is taught by Cooper et al. in U.S. Pat. No. 5,892,228 (Process and apparatus for octane numbers and Reid vapor pressure by Raman spectroscopy, 1999). A linear regression model, partial least squares (PLS), was used to correlate various parts of the Raman spectra to these properties. In essence Cooper added more Raman spectral features to those employed by Clark to improve the accuracy of the predicted values. This required measuring the Raman spectra of several hundred fuel samples with known octane and vapor pressure values to establish a statistical basis for the correlations.
In a similar manner, Williams et al. used chemometrics to establish a relationship between cetane values (index and number) and Raman spectra for some 18 diesel samples (“Determination of Gas Oil Cetane Number and Cetane Index Using Near-Infrared Fourier Transform Raman Spectroscopy,” Anal. Chem., 62, 2553-2556, 1990). Again, the success of this approach can be explained by the fact that the cetane number is defined by the relative proportions of n-hexadecane (cetane) and alpha-methylnaphthalene. These researchers also show that various principal components of correlation (one positive, one negative) look nearly identical to the Raman spectra for these two chemicals. In fact, it is clear that a ratio of the 1378 cm−1 naphthalene Raman peak to the 1445 cetane CH2 wag could be correlated to the cetane number of these diesel samples.
In the case of determining the chemical composition of fuels and developing correlations, there are two limitations associated with most commercial Raman analyzers and all portable Raman analyzers. First, they employ excitation lasers that use visible wavelengths, which generate fluorescence in many fuels, especially diesels. This fluorescence often obscures the Raman spectrum, which in turn eliminates the possibility of determinating chemical composition or developing property correlations. However, the availability of near-infrared wavelength lasers, the most common being neodymium-based lasers that emit at 1064 nm, allows overcoming this difficulty, as they rarely generate fluorescence in the sample. Second, these analyzers use array-based detectors, which can not maintain x-axis stability or reproducibility. Changes in ambient temperature cause distortions in the optics that are used to separate the Raman spectrum into its component wavelengths, usually gratings. For example, an increase in temperature will expand the grating causing the spectrum to expand across the detector in an accordion fashion. Furthermore, the conversion efficiency of photons to electrons for each detector element in the array is slightly different. And, the charge generated in each element can “bleed” to adjacent elements, and the amount of this bleeding changes with the amount of photons hitting the detector element. Consequently, the spectral response for one array of detector elements is different from another and changes with measurement conditions (e.g. the temperature and the intensity of the Raman spectrum measured). Bowie, et al. measured and reported these limitations in (“Factors affecting the performance of bench-top Raman spectrometers.”Appl. Spec, 54, 164A-173A, 2000). Unfortunately, successful use of chemometric models requires absolute stability and reproducibility in the x-axis.
It is worth stating that similar correlations between near-infrared (NIR) spectroscopy and gasoline properties have also been developed. Kelly et al. showed that the major components of gasoline could be determined by NIR (“Nondestructive Analytical Procedure for Simultaneous Estimation of the Major Classes of Hydrocarbon Constituents of Finished Gasolines,”Anal. Chem., 62, 1444-1451, 1990), and that this information could be used to predict octane numbers (“Prediction of gasoline octane numbers from near-infrared spectral features in the range of 660-1215 nm”, Anal. Chem. 61, 313-320, 1989). Patents granted to Maggard disclose these ideas (U.S. Pat. No. 4,963,745, “Octane measuring process and device”, 1990, and U.S. Pat. No. 5,349,188, “Near infrared analysis of PIANO constituents and octane number of hydrocarbons”, 1994, where PIANO stands for paraffin, isoparaffin, aromatic, napthenes and olefins).
It is clear from the forgoing that the correlations between Raman spectra and fuel properties, specifically octane, cetane and vapor values, are based on identifiable chemical composition, in this case, phenyl, biphenyl and butane content, respectively. Such properties are defined herein as chemical-based properties. The foregoing does not teach the use of chemometrics applied to Raman or NIR spectra to determine other important fuel properties such as physical state changes (e.g. freezing and boiling point), heat of combustion, lubricity, thermal stability or viscosity. Nor does the foregoing teach the use of chemometrics to compensate for temperature changes in the sample, instrument, or the ambient environment. Nor does the foregoing teach the use of chemometrics to transfer the correlation model from one spectrometer to another. Nor does the forgoing teach the use of chemometrics to distinguish one class of fuel from another, such as gasoline versus diesel versus jet fuel, or distinguish grade of fuel within a class, such as Jet A from Jet A 1 from JP-5, etc. Nor does the forgoing teach the use of Raman-based chemometric models to control fuel class, grade or properties during distillation of petroleum at refineries, or control yield in a biodiesel reactor. Nor does the foregoing teach the use of chemometrics to identify the class or grade of an unknown fuel. Nor does the foregoing teach the use of a coarse model to identify unknown fuels by class and a refined model to better predict its properties. Nor does the foregoing teach the use of a model to identify fuel by grade and a further reined model to better predict its properties. Nor does the foregoing teach the use of refined models that correct for temperature, x- and y-axis variations, modified or mixed fuels to better predict properties.